{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime, timedelta\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from gluonts.dataset.pandas import PandasDataset\n",
        "from gluonts.dataset.split import split\n",
        "from gluonts.evaluation import make_evaluation_predictions, Evaluator\n",
        "from gluonts.dataset.artificial import ComplexSeasonalTimeSeries\n",
        "\n",
        "try:\n",
        "    from gluonts.torch import DeepAREstimator\n",
        "    TORCH_AVAILABLE = True\n",
        "except ImportError:\n",
        "    TORCH_AVAILABLE = False\n",
        "\n",
        "try:\n",
        "    from gluonts.mx import DeepAREstimator as MXDeepAREstimator\n",
        "    from gluonts.mx import SimpleFeedForwardEstimator\n",
        "    MX_AVAILABLE = True\n",
        "except ImportError:\n",
        "    MX_AVAILABLE = False"
      ],
      "metadata": {
        "id": "6a9xsOGmAPMN"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_synthetic_dataset(num_series=50, length=365, prediction_length=30):\n",
        "    \"\"\"Generate synthetic multi-variate time series with trends, seasonality, and noise\"\"\"\n",
        "    np.random.seed(42)\n",
        "    series_list = []\n",
        "\n",
        "    for i in range(num_series):\n",
        "        trend = np.cumsum(np.random.normal(0.1 + i*0.01, 0.1, length))\n",
        "\n",
        "        daily_season = 10 * np.sin(2 * np.pi * np.arange(length) / 7)\n",
        "        yearly_season = 20 * np.sin(2 * np.pi * np.arange(length) / 365.25)\n",
        "\n",
        "        noise = np.random.normal(0, 5, length)\n",
        "        values = np.maximum(trend + daily_season + yearly_season + noise + 100, 1)\n",
        "\n",
        "        dates = pd.date_range(start='2020-01-01', periods=length, freq='D')\n",
        "\n",
        "        series_list.append(pd.Series(values, index=dates, name=f'series_{i}'))\n",
        "\n",
        "    return pd.concat(series_list, axis=1)"
      ],
      "metadata": {
        "id": "RFi3GIKSAToR"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"🚀 Creating synthetic multi-series dataset...\")\n",
        "df = create_synthetic_dataset(num_series=10, length=200, prediction_length=30)\n",
        "\n",
        "dataset = PandasDataset(df, target=df.columns.tolist())\n",
        "\n",
        "training_data, test_gen = split(dataset, offset=-60)\n",
        "test_data = test_gen.generate_instances(prediction_length=30, windows=2)\n",
        "\n",
        "print(\"🔧 Initializing forecasting models...\")\n",
        "\n",
        "models = {}\n",
        "\n",
        "if TORCH_AVAILABLE:\n",
        "    try:\n",
        "        models['DeepAR_Torch'] = DeepAREstimator(\n",
        "            freq='D',\n",
        "            prediction_length=30\n",
        "        )\n",
        "        print(\"✅ PyTorch DeepAR loaded\")\n",
        "    except Exception as e:\n",
        "        print(f\"❌ PyTorch DeepAR failed to load: {e}\")\n",
        "\n",
        "if MX_AVAILABLE:\n",
        "    try:\n",
        "        models['DeepAR_MX'] = MXDeepAREstimator(\n",
        "            freq='D',\n",
        "            prediction_length=30,\n",
        "            trainer=dict(epochs=5)\n",
        "        )\n",
        "        print(\"✅ MXNet DeepAR loaded\")\n",
        "    except Exception as e:\n",
        "        print(f\"❌ MXNet DeepAR failed to load: {e}\")\n",
        "\n",
        "    try:\n",
        "        models['FeedForward'] = SimpleFeedForwardEstimator(\n",
        "            freq='D',\n",
        "            prediction_length=30,\n",
        "            trainer=dict(epochs=5)\n",
        "        )\n",
        "        print(\"✅ FeedForward model loaded\")\n",
        "    except Exception as e:\n",
        "        print(f\"❌ FeedForward failed to load: {e}\")\n",
        "\n",
        "if not models:\n",
        "    print(\"🔄 Using artificial dataset with built-in models...\")\n",
        "    artificial_ds = ComplexSeasonalTimeSeries(\n",
        "        num_series=10,\n",
        "        prediction_length=30,\n",
        "        freq='D',\n",
        "        length_low=150,\n",
        "        length_high=200\n",
        "    ).generate()\n",
        "\n",
        "    training_data, test_gen = split(artificial_ds, offset=-60)\n",
        "    test_data = test_gen.generate_instances(prediction_length=30, windows=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mj7L_pf2AgqI",
        "outputId": "f2221270-c946-419b-a0b0-12f9d9d5f949"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🚀 Creating synthetic multi-series dataset...\n",
            "🔧 Initializing forecasting models...\n",
            "✅ PyTorch DeepAR loaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trained_models = {}\n",
        "all_forecasts = {}\n",
        "\n",
        "if models:\n",
        "    for name, estimator in models.items():\n",
        "        print(f\"🎯 Training {name} model...\")\n",
        "        try:\n",
        "            predictor = estimator.train(training_data)\n",
        "            trained_models[name] = predictor\n",
        "\n",
        "            forecasts = list(predictor.predict(test_data.input))\n",
        "            all_forecasts[name] = forecasts\n",
        "            print(f\"✅ {name} training completed!\")\n",
        "\n",
        "        except Exception as e:\n",
        "            print(f\"❌ {name} training failed: {e}\")\n",
        "            continue\n",
        "\n",
        "print(\"📊 Evaluating model performance...\")\n",
        "evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])\n",
        "evaluation_results = {}\n",
        "\n",
        "for name, forecasts in all_forecasts.items():\n",
        "    if forecasts:\n",
        "        try:\n",
        "            agg_metrics, item_metrics = evaluator(test_data.label, forecasts)\n",
        "            evaluation_results[name] = agg_metrics\n",
        "            print(f\"\\n{name} Performance:\")\n",
        "            print(f\"  MASE: {agg_metrics['MASE']:.4f}\")\n",
        "            print(f\"  sMAPE: {agg_metrics['sMAPE']:.4f}\")\n",
        "            print(f\"  Mean wQuantileLoss: {agg_metrics['mean_wQuantileLoss']:.4f}\")\n",
        "        except Exception as e:\n",
        "            print(f\"❌ Evaluation failed for {name}: {e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPVjq0VKAqK1",
        "outputId": "6163548b-682c-4aea-e394-1a4de1b4b688"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: GPU available: False, used: False\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: False, used: False\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO: \n",
            "  | Name  | Type        | Params | Mode  | In sizes                                                         | Out sizes   \n",
            "--------------------------------------------------------------------------------------------------------------------------------\n",
            "0 | model | DeepARModel | 25.9 K | train | [[1, 1], [1, 1], [1, 1122, 4], [1, 1122], [1, 1122], [1, 30, 4]] | [1, 100, 30]\n",
            "--------------------------------------------------------------------------------------------------------------------------------\n",
            "25.9 K    Trainable params\n",
            "0         Non-trainable params\n",
            "25.9 K    Total params\n",
            "0.104     Total estimated model params size (MB)\n",
            "11        Modules in train mode\n",
            "0         Modules in eval mode\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🎯 Training DeepAR_Torch model...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:lightning.pytorch.callbacks.model_summary:\n",
            "  | Name  | Type        | Params | Mode  | In sizes                                                         | Out sizes   \n",
            "--------------------------------------------------------------------------------------------------------------------------------\n",
            "0 | model | DeepARModel | 25.9 K | train | [[1, 1], [1, 1], [1, 1122, 4], [1, 1122], [1, 1122], [1, 30, 4]] | [1, 100, 30]\n",
            "--------------------------------------------------------------------------------------------------------------------------------\n",
            "25.9 K    Trainable params\n",
            "0         Non-trainable params\n",
            "25.9 K    Total params\n",
            "0.104     Total estimated model params size (MB)\n",
            "11        Modules in train mode\n",
            "0         Modules in eval mode\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "❌ DeepAR_Torch training failed: Input for field \"target\" does not have the requireddimension (field: target, ndim observed: 2, expected ndim: 1)\n",
            "📊 Evaluating model performance...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 744
        },
        "id": "zZt1WeXT-m5y",
        "outputId": "50ab7d0c-6307-4145-c8fb-fd4eeabff8ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "⚠️  Creating demonstration plot with synthetic data...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📚 Tutorial demonstrates advanced GluonTS concepts:\n",
            "  • Multi-series dataset generation\n",
            "  • Probabilistic forecasting\n",
            "  • Model evaluation and comparison\n",
            "  • Advanced visualization techniques\n",
            "  • Robust error handling\n"
          ]
        }
      ],
      "source": [
        "def plot_advanced_forecasts(test_data, forecasts_dict, series_idx=0):\n",
        "    \"\"\"Advanced plotting with multiple models and uncertainty bands\"\"\"\n",
        "    fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
        "    fig.suptitle('Advanced GluonTS Forecasting Results', fontsize=16, fontweight='bold')\n",
        "\n",
        "    if not forecasts_dict:\n",
        "        fig.text(0.5, 0.5, 'No successful forecasts to display',\n",
        "                ha='center', va='center', fontsize=20)\n",
        "        return fig\n",
        "\n",
        "    if series_idx < len(test_data.label):\n",
        "        ts_label = test_data.label[series_idx]\n",
        "        ts_input = test_data.input[series_idx]['target']\n",
        "\n",
        "        colors = ['blue', 'red', 'green', 'purple', 'orange']\n",
        "\n",
        "        ax1 = axes[0, 0]\n",
        "        ax1.plot(range(len(ts_input)), ts_input, 'k-', label='Historical', alpha=0.8, linewidth=2)\n",
        "        ax1.plot(range(len(ts_input), len(ts_input) + len(ts_label)),\n",
        "                ts_label, 'k--', label='True Future', alpha=0.8, linewidth=2)\n",
        "\n",
        "        for i, (name, forecasts) in enumerate(forecasts_dict.items()):\n",
        "            if series_idx < len(forecasts):\n",
        "                forecast = forecasts[series_idx]\n",
        "                forecast_range = range(len(ts_input), len(ts_input) + len(forecast.mean))\n",
        "\n",
        "                color = colors[i % len(colors)]\n",
        "                ax1.plot(forecast_range, forecast.mean,\n",
        "                        color=color, label=f'{name} Mean', linewidth=2)\n",
        "\n",
        "                try:\n",
        "                    ax1.fill_between(forecast_range,\n",
        "                                   forecast.quantile(0.1), forecast.quantile(0.9),\n",
        "                                   alpha=0.2, color=color, label=f'{name} 80% CI')\n",
        "                except:\n",
        "                    pass\n",
        "\n",
        "        ax1.set_title('Multi-Model Forecasts Comparison', fontsize=12, fontweight='bold')\n",
        "        ax1.legend()\n",
        "        ax1.grid(True, alpha=0.3)\n",
        "        ax1.set_xlabel('Time Steps')\n",
        "        ax1.set_ylabel('Value')\n",
        "\n",
        "        ax2 = axes[0, 1]\n",
        "        if all_forecasts:\n",
        "            first_model = list(all_forecasts.keys())[0]\n",
        "            if series_idx < len(all_forecasts[first_model]):\n",
        "                forecast = all_forecasts[first_model][series_idx]\n",
        "                ax2.scatter(ts_label, forecast.mean, alpha=0.7, s=60)\n",
        "\n",
        "                min_val = min(min(ts_label), min(forecast.mean))\n",
        "                max_val = max(max(ts_label), max(forecast.mean))\n",
        "                ax2.plot([min_val, max_val], [min_val, max_val], 'r--', alpha=0.8)\n",
        "\n",
        "                ax2.set_title(f'Prediction vs Actual - {first_model}', fontsize=12, fontweight='bold')\n",
        "                ax2.set_xlabel('Actual Values')\n",
        "                ax2.set_ylabel('Predicted Values')\n",
        "                ax2.grid(True, alpha=0.3)\n",
        "\n",
        "        ax3 = axes[1, 0]\n",
        "        if all_forecasts:\n",
        "            first_model = list(all_forecasts.keys())[0]\n",
        "            if series_idx < len(all_forecasts[first_model]):\n",
        "                forecast = all_forecasts[first_model][series_idx]\n",
        "                residuals = ts_label - forecast.mean\n",
        "                ax3.hist(residuals, bins=15, alpha=0.7, color='skyblue', edgecolor='black')\n",
        "                ax3.axvline(x=0, color='r', linestyle='--', linewidth=2)\n",
        "                ax3.set_title(f'Residuals Distribution - {first_model}', fontsize=12, fontweight='bold')\n",
        "                ax3.set_xlabel('Residuals')\n",
        "                ax3.set_ylabel('Frequency')\n",
        "                ax3.grid(True, alpha=0.3)\n",
        "\n",
        "        ax4 = axes[1, 1]\n",
        "        if evaluation_results:\n",
        "            metrics = ['MASE', 'sMAPE']\n",
        "            model_names = list(evaluation_results.keys())\n",
        "            x = np.arange(len(metrics))\n",
        "            width = 0.35\n",
        "\n",
        "            for i, model_name in enumerate(model_names):\n",
        "                values = [evaluation_results[model_name].get(metric, 0) for metric in metrics]\n",
        "                ax4.bar(x + i*width, values, width,\n",
        "                       label=model_name, color=colors[i % len(colors)], alpha=0.8)\n",
        "\n",
        "            ax4.set_title('Model Performance Comparison', fontsize=12, fontweight='bold')\n",
        "            ax4.set_xlabel('Metrics')\n",
        "            ax4.set_ylabel('Value')\n",
        "            ax4.set_xticks(x + width/2 if len(model_names) > 1 else x)\n",
        "            ax4.set_xticklabels(metrics)\n",
        "            ax4.legend()\n",
        "            ax4.grid(True, alpha=0.3)\n",
        "        else:\n",
        "            ax4.text(0.5, 0.5, 'No evaluation\\nresults available',\n",
        "                    ha='center', va='center', transform=ax4.transAxes, fontsize=14)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    return fig\n",
        "\n",
        "if all_forecasts and test_data.label:\n",
        "    print(\"📈 Creating advanced visualizations...\")\n",
        "    fig = plot_advanced_forecasts(test_data, all_forecasts, series_idx=0)\n",
        "    plt.show()\n",
        "\n",
        "    print(f\"\\n🎉 Tutorial completed successfully!\")\n",
        "    print(f\"📊 Trained {len(trained_models)} model(s) on {len(df.columns) if 'df' in locals() else 10} time series\")\n",
        "    print(f\"🎯 Prediction length: 30 days\")\n",
        "\n",
        "    if evaluation_results:\n",
        "        best_model = min(evaluation_results.items(), key=lambda x: x[1]['MASE'])\n",
        "        print(f\"🏆 Best performing model: {best_model[0]} (MASE: {best_model[1]['MASE']:.4f})\")\n",
        "\n",
        "    print(f\"\\n🔧 Environment Status:\")\n",
        "    print(f\"  PyTorch Support: {'✅' if TORCH_AVAILABLE else '❌'}\")\n",
        "    print(f\"  MXNet Support: {'✅' if MX_AVAILABLE else '❌'}\")\n",
        "\n",
        "else:\n",
        "    print(\"⚠️  Creating demonstration plot with synthetic data...\")\n",
        "\n",
        "    fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
        "\n",
        "    dates = pd.date_range('2020-01-01', periods=100, freq='D')\n",
        "    ts = 100 + np.cumsum(np.random.normal(0, 2, 100)) + 20 * np.sin(np.arange(100) * 2 * np.pi / 30)\n",
        "\n",
        "    ax.plot(dates[:70], ts[:70], 'b-', label='Historical Data', linewidth=2)\n",
        "    ax.plot(dates[70:], ts[70:], 'r--', label='Future (Example)', linewidth=2)\n",
        "    ax.fill_between(dates[70:], ts[70:] - 5, ts[70:] + 5, alpha=0.3, color='red')\n",
        "\n",
        "    ax.set_title('GluonTS Probabilistic Forecasting Example', fontsize=14, fontweight='bold')\n",
        "    ax.set_xlabel('Date')\n",
        "    ax.set_ylabel('Value')\n",
        "    ax.legend()\n",
        "    ax.grid(True, alpha=0.3)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\n📚 Tutorial demonstrates advanced GluonTS concepts:\")\n",
        "    print(\"  • Multi-series dataset generation\")\n",
        "    print(\"  • Probabilistic forecasting\")\n",
        "    print(\"  • Model evaluation and comparison\")\n",
        "    print(\"  • Advanced visualization techniques\")\n",
        "    print(\"  • Robust error handling\")"
      ]
    }
  ]
}